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Methods of Identification in Social Networks

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  • Bryan S. Graham

    () (Department of Economics, University of California, Berkeley, California 94720-3888
    National Bureau of Economic Research, Cambridge, Massachusetts 02138)

Abstract

Social and economic networks are ubiquitous, serving as contexts for job search, technology diffusion, the accumulation of human capital, and even the formulation of norms and values. The systematic empirical study of network formation—the process by which agents form, maintain, and dissolve links—within economics is recent, is associated with extraordinarily challenging modeling and identification issues, and is an area of exciting new developments, with many open questions. This article reviews prominent research on the empirical analysis of network formation, with an emphasis on contributions made by economists.

Suggested Citation

  • Bryan S. Graham, 2015. "Methods of Identification in Social Networks," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 465-485, August.
  • Handle: RePEc:anr:reveco:v:7:y:2015:p:465-485
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    References listed on IDEAS

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    Cited by:

    1. Konstantin Büchel und Maximilian von Ehrlich, 2016. "Cities and the Structure of Social Interactions: Evidence from Mobile Phone Data," Diskussionsschriften dp1608, Universitaet Bern, Departement Volkswirtschaft.
    2. repec:eee:jeborg:v:141:y:2017:i:c:p:233-253 is not listed on IDEAS
    3. Firmin Doko Tchatoka & Robert Garrard & Virginie Masson, 2017. "Testing for Stochastic Dominance in Social Networks," School of Economics Working Papers 2017-02, University of Adelaide, School of Economics.
    4. repec:eee:phsmap:v:489:y:2018:i:c:p:102-111 is not listed on IDEAS
    5. Patacchini, Eleonora & Arduini, Tiziano, 2016. "Residential choices of young Americans," Journal of Housing Economics, Elsevier, vol. 34(C), pages 69-81.
    6. Ioannides, Yannis M., 2015. "Neighborhoods to nations via social interactions," Economic Modelling, Elsevier, vol. 48(C), pages 5-15.
    7. Vincent Boucher & Bernard Fortin, 2015. "Some Challenges in the Empirics of the Effects of Networks," Cahiers de recherche 1504, CIRPEE.
    8. repec:spr:sjecst:v:154:y:2018:i:1:d:10.1186_s41937-017-0011-x is not listed on IDEAS
    9. George Judge, 2016. "Econometric Information Recovery in Behavioral Networks," Econometrics, MDPI, Open Access Journal, vol. 4(3), pages 1-11, September.
    10. Gaudeul, Alexia & Giannetti, Caterina, 2015. "Privacy, trust and social network formation," Center for European, Governance and Economic Development Research Discussion Papers 269, University of Goettingen, Department of Economics.
    11. Bryan S. Graham, 2014. "An econometric model of link formation with degree heterogeneity," NBER Working Papers 20341, National Bureau of Economic Research, Inc.
    12. Chih-Sheng Hsieh & Michael D. König & Xiaodong Liu, 2012. "Network formation with local complements and global substitutes: the case of R&D networks," ECON - Working Papers 217, Department of Economics - University of Zurich, revised Feb 2017.
    13. Bryan S. Graham, 2016. "Homophily and transitivity in dynamic network formation," CeMMAP working papers CWP16/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Jackson, Matthew O. & Rogers, Brian & Zenou, Yves, 2016. "Networks: An economic perspective," CEPR Discussion Papers 11452, C.E.P.R. Discussion Papers.
    15. Luisa Corrado & Salvatore Di Novo, 2018. "Estimating Models with Dynamic Network Interactions and Unobserved Heterogeneity," CEIS Research Paper 439, Tor Vergata University, CEIS, revised 09 Aug 2018.
    16. Quintana-Domeque, Climent & Wohlfart, Johannes, 2016. "“Relative concerns for consumption at the top”: An intertemporal analysis for the UK," Journal of Economic Behavior & Organization, Elsevier, vol. 129(C), pages 172-194.
    17. Kummer, Michael E. & Saam, Marianne & Halatchliyski, Iassen & Giorgidze, George, 2016. "Centrality and content creation in networks - The case of economic topics on German wikipedia," Information Economics and Policy, Elsevier, vol. 36(C), pages 36-52.
    18. Patacchini, Eleonora & Rainone, Edoardo & Zenou, Yves, 2017. "Heterogeneous peer effects in education," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 190-227.
    19. Bryan S. Graham, 2017. "An econometric model of network formation with degree heterogeneity," CeMMAP working papers CWP08/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Ida Johnsson & Hyungsik Roger Moon, 2017. "Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach," Papers 1709.10024, arXiv.org.
    21. Fernando Linardi & Cees (C.G.H.) Diks & Marco (M.J.) van der Leij & Iuri Lazier, 2017. "Dynamic Interbank Network Analysis Using Latent Space Models," Tinbergen Institute Discussion Papers 17-101/II, Tinbergen Institute.

    More about this item

    Keywords

    strategic network formation; homophily; transitivity; heterogeneity; peer effects;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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